David Ha

According to our database, David Ha authored at least 36 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Evolutionary Optimization of Model Merging Recipes.

Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha

CoRR, 2024

2022

Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter.

Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber

CoRR, 2022

Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport.

Yingtao Tian, Marco Cuturi, David Ha

Proceedings of the 13th International Conference on Computational Creativity, Bozen-Bolzano, Italy, June 27, 2022

EvoJAX: hardware-accelerated neuroevolution.

Yujin Tang, Yingtao Tian, David Ha

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Evolving modular soft robots without explicit inter-module communication using local self-attention.

Federico Pigozzi, Yujin Tang, Eric Medvet, David Ha

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Boston, Massachusetts, USA, July 9, 2022

Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts.

Yingtao Tian, David Ha

Proceedings of the Artificial Intelligence in Music, Sound, Art and Design, 2022

2021

An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation.

David Ha, Shigeru Katagiri, Hideyuki Watanabe, Miho Ohsaki

J. Signal Process. Syst., 2021

Collective Intelligence for Deep Learning: A Survey of Recent Developments.

David Ha, Yujin Tang

CoRR, 2021

Sketch-based Creativity Support Tools using Deep Learning.

Forrest Huang, Eldon Schoop, David Ha, Jeffrey Nichols, John F. Canny

CoRR, 2021

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning.

Yujin Tang, David Ha

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

World Models and Attention for Reinforcement Learning.

David Ha

Proceedings of the 2021 Conference on Artificial Life, 2021

2020

A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection.

David Ha, Yuya Tomotoshi, Masahiro Senda, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki

J. Signal Process. Syst., 2020

SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks.

Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha

Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020

Scones: towards conversational authoring of sketches.

Forrest Huang, Eldon Schoop, David Ha, John F. Canny

Proceedings of the IUI '20: 25th International Conference on Intelligent User Interfaces, 2020

Neuroevolution of self-interpretable agents.

Yujin Tang, Duong Nguyen, David Ha

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error.

Miguel González Duque, Rasmus Berg Palm, David Ha, Sebastian Risi

Proceedings of the IEEE Conference on Games, 2020

2019

Reinforcement Learning for Improving Agent Design.

David Ha

Artif. Life, 2019

Maximum Bayes Boundary-Ness Training For Pattern Classification.

Masahiro Senda, David Ha, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki

Proceedings of the 2nd International Conference on Signal Processing and Machine Learning, 2019

Weight Agnostic Neural Networks.

Adam Gaier, David Ha

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction.

C. Daniel Freeman, David Ha, Luke Metz

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers.

Yuya Tomotoshi, David Ha, Emilie Delattre, Hideyuki Watanabe, Xugang Lu, Shigeru Katagiri, Miho Ohsaki

Proceedings of the Integrated Uncertainty in Knowledge Modelling and Decision Making, 2019

Learning Latent Dynamics for Planning from Pixels.

Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson

Proceedings of the 36th International Conference on Machine Learning, 2019

A Learned Representation for Scalable Vector Graphics.

Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

collabdraw: An Environment for Collaborative Sketching with an Artificial Agent.

Judith E. Fan, Monica Dinculescu, David Ha

Proceedings of the 2019 ACM SIGCHI Conference on Creativity and Cognition, 2019

2018

Deep Learning for Classical Japanese Literature.

Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha

CoRR, 2018

World Models.

David Ha, Jürgen Schmidhuber

CoRR, 2018

Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method.

David Ha, Hideyuki Watanabe, Yuya Tomotoshi, Emilie Delattre, Shigeru Katagiri

Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, 2018

Recurrent World Models Facilitate Policy Evolution.

David Ha, Jürgen Schmidhuber

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Optimal Classifier Model Status Selection using Bayes boundary uncertainty.

David Ha, Emilie Delattre, Yuya Tomotoshi, Masahiro Senda, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki

Proceedings of the 28th IEEE International Workshop on Machine Learning for Signal Processing, 2018

Learning via Social Awareness: Improving a Deep Generative Sketching Model with Facial Feedback.

Natasha Jaques, Jennifer McCleary, Jesse H. Engel, David Ha, Fred Bertsch, Douglas Eck, Rosalind W. Picard

Proceedings of the Workshop on Artificial Intelligence in Affective Computing, 2018

Learning via social awareness: improving sketch representations with facial feedback.

Natasha Jaques, Jesse H. Engel, David Ha, Fred Bertsch, Rosalind W. Picard, Douglas Eck

Proceedings of the 6th International Conference on Learning Representations, 2018

A Neural Representation of Sketch Drawings.

David Ha, Douglas Eck

Proceedings of the 6th International Conference on Learning Representations, 2018

Neuroevolution for deep reinforcement learning problems.

David Ha

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018

Pommerman: A Multi-Agent Playground.

Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob N. Foerster, Julian Togelius, Kyunghyun Cho, Joan Bruna

Proceedings of the Joint Proceedings of the AIIDE 2018 Workshops co-located with 14th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2018), 2018

2017

PathNet: Evolution Channels Gradient Descent in Super Neural Networks.

Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra

CoRR, 2017

HyperNetworks.

David Ha, Andrew M. Dai, Quoc V. Le

Proceedings of the 5th International Conference on Learning Representations, 2017